A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Cecilia Ka Yuk Chan, Wenjie Hu
Survey of 399 Hong Kong university students reveals positive attitudes toward generative AI in higher education, highlighting benefits like personalized learning and concerns about accuracy, privacy, and ethics.
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WorldArena is a unified benchmark for evaluating embodied world models across perception and functional utility, addressing fragmented evaluation.
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The paper introduces a training approach to enhance fine-grained visual perception in vision-language models, reducing visual biases and improving detail recognition.
Dexter Ong, Vijay Kumar, Pratik Chaudhari
EVPeriscope uses an upward-facing event camera on a ground robot to detect a quadrotor's propeller signature for relative localization and control in degraded conditions.
Bobo Li, Hao Fei, Tianjie Ju, et al.
OmniScientist is an end-to-end omni-modal AI scientist that conducts multidisciplinary research directly from heterogeneous raw evidence via a perception layer and three autonomous agents.
Lin Zhang, Lei Zhang, Xuanqin Mou, et al.
FSIM proposes a full-reference image quality metric using phase congruency and gradient magnitude as complementary features, achieving state-of-the-art correlation with human perception.
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This paper presents a language-guided long-horizon manipulation framework for multistep cloth folding, combining LLM-based planning with visual perception.
Zhen Fang, Yu Zeng, Wenxuan Huang, et al.
Video-DeepResearch extends multimodal agents to continuous video streams with a decoupled perception-exploration pipeline, achieving SOTA 64.0% on a new benchmark.
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Introduces Blink, a benchmark for multimodal LLMs focusing on core visual perception abilities, with tasks that are easy for humans but challenging for current models.
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Introduces Action Reasoning Models (ARMs), a robotic foundation model class with a structured three-stage pipeline integrating perception, planning, and control.
Simon Bruno Egli, Armon Arpagaus, Simon Adrian Amacher, et al.
A cross-sectional survey of Swiss clinicians reveals frequent LLM use (32.8%) with limited workplace guidelines, highlighting gaps in safe adoption.
Lizhi Yang, Junheng Li, Aaron D. Ames
PAC-MAN couples control-barrier safety with perception-aware training for whole-body humanoid dodgeball, achieving 95% real-world evasion with a fixed camera.